Download Growth Impacts of the Exchange Rate and Technology

1
Growth Impacts of the Exchange Rate and
Technology
ALEJANDRO MÁRQUEZ-VELÁZQUEZ
Institute for Latin American Studies, Freie Universität Berlin 1
October 6, 2015
The aim of this paper is to assess whether the impacts of real exchange rate undervaluation
and domestic technological capabilities on growth are stable across development levels. Time-seriescross-section-growth regressions with development level interactions are used to test the stability of
these variables’ growth impact. The results show that real undervaluation is a growth driver across all
development levels, once technological capabilities are accounted for; however, it is more important
for developing and developed countries than for emerging markets. The results also suggest that
developing countries grow faster when they are globally competitive in low-technology manufacturing
and natural-resource-intensive industries.
Keywords Economic development, economic growth, domestic technological capabilities, real
exchange rate undervaluation, time-series-cross-section regressions, trade patterns.
JEL Classification: O12, O14, O33, O47
1
Rüdesheimer Str. 54/56, 14197 Berlin, Germany. Telephone: + 49 30 838 72644. Fax: +49 30 838 55464. Email:
[email protected]. I am grateful for the comments received from Christian Ambrosius, Carsten Dreher,
Sebastian Dullien, Barbara Fritz, Carmen Marull Maita and Manfred Nitsch.
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ALEJANDRO MÁRQUEZ-VELAZQUEZ
1. INTRODUCTION
Rodrik (2008) provides empirical evidence and develops theories about the importance for
developing countries of real exchange rate (RER) undervaluation as a growth-driving factor. The
author suggests that the transmission channel leading to higher growth rates is the increase in the
relative price of tradables that RER undervaluation (RERU) implies, which should incentivize
investment in modern tradable sectors. The author argues that these sectors are underdeveloped in
these countries because they are disproportionally affected by market and government failures. In
contrast, the author finds that the growth impact of RERU is not significant in the case of developed
countries. He explains these results as a consequence of the lower sensitivity of modern tradable
sectors to RERU, given that market and government failures therein are less prevalent in developed
countries. Nevertheless, by replicating Rodrik’s approach yet considering a third intermediate
development level Rapetti, Skott and Razmi (2012) report a significant growth impact of RERU in
developed and developing countries, although not for the case of emerging countries. The authors are
surprised by the lack of significance of RERU’s growth impact in this latter group of countries,
labeling it ‘both a theoretical and empirical puzzle’ (Rapetti et al., 2012, p. 14).
Upon first consideration, it is not obvious that higher RERU should increase growth rates, since
increases in RERU imply real depreciations, which in isolation do not always translate into growth.
Many empirical studies using the before-after approach to analyze depreciations’ output impact have
failed to find a positive growth impact due to their inability to control for other factors that might
affect output during depreciation episodes. To control for this, Agénor (1991) considered that there are
two types of depreciations, expected and unexpected. Using time-series-cross-section (TSCS)
regressions following this insight, Agénor finds that all the negative impacts of depreciations can be
attributed to expected depreciations that occur owing to high RER overvaluation (RERO). RER
depreciations should thus spur growth whenever they are not the result of steep RER corrections
following large RERO episodes.
Unfortunately, the discussion presented thus far has overlooked the fact that all countries have
differing domestic technological capabilities (DTC). These capabilities can be defined as the degree to
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which natural-resource-intensive industries and low-, mid- and high-technology-manufacturing
industries are economically viable within a country.2 Given that technology has been considered the
major growth driver in most growth theories (Fagerberg, 1987), a relative increase in the global
competitiveness of a country’s mid- and high-technology industries should increase its growth rate,
ceteris paribus. Historically, the successive wave of countries on sustained economic development
paths implemented a set of policies aimed at fostering the development of DTC in mid- and hightechnological capabilities (Amsden, 2001, 1989; Lall, 2000). However, many of the developing
countries that have failed to sustain long-term catch-up growth also implemented similar policies
(Commission on Growth and Development, 2008, p. 48).
The difference between success and failure cases is that only the successful catching-up
economies have been able to sustainably shift their economic structure away from natural-resourceintensive and low-technology manufactures towards mid- and high-technology sectors. While a strand
of the political economy literature focuses on the importance of industrial and technological policies
with an appropriate mix of stick-and-carrot incentives that aim to build up internationally competitive
mid- and high-technology sectors (Kanchoochat, in press), this study follows Lin’s (2012) “new
structural economics” view. This view suggests that the type of industries to be promoted needs to be
related to the level of countries’ DTC. Despite the debate surrounding how to identify such industries
(Lin and Chang, 2012), one can argue that this view represents a rupture with the conventional neoclassic view, which does not give any relevance to the economic structure of countries.
By contrast, one can expect that the path towards high sustained growth rates for a country with
low DTC begins by specializing in natural-resource or low-technology-manufacturing industries that
are labor-intensive and less knowledge-intensive, according to the view advanced in this piece of
research. Such a development strategy has four major benefits for low-income countries: it generates
high employment levels, higher back- and forward linkages, less dependence on imported inputs, as
well as lower learning and investment needs, at least when compared to mid- and high-technology
2
Sectors can be classified as low-, mid- and high-technology depending on the intensiveness of R&D expenditure
associated to them in developed countries (ECLAC, 2011), whose industries tend to be close to the technological frontier. A
similar technological classification of sectors was proposed by Lall (2000).
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manufacturing sectors. Therefore, such a development strategy will probably be responsible for highincome multipliers and a lower balance-of-payments (BoP) constraint, which should pave the way for
a high growth rate.
This article aims to test two major hypotheses derived from the previous explanation. The first one
is to assess whether low-income countries — which one can assume to generally have low DTC —
have grown faster when specializing in natural-resource-intensive and low-technology-manufacturing
industries. A related question is to establish whether middle-income countries — or the so-called
emerging markets3 — have achieved higher growth by increasing the relative importance of mid- and
high-technology sectors in their economies. The second hypothesis to test is whether the impact of
RERU on growth has been the same across development levels, once DTC are controlled for.
This paper contends that Rodrik (2008) did not obtain a positive and significant growth impact of
RERU for his sample of developed countries because he used an income level that is too low to
classify a country as developed. If he had used a higher income level as a threshold, he would have
found that RERU also has a positive and significant growth impact, as in Rapetti et al. (2012).
Moreover, this paper argues that Rapetti et al. (2012) could not find a significant and positive growth
impact of RERU for emerging countries because they did not control for DTC. Such an omission is
relevant since one can expect that RERU should be a more relevant growth driver for emerging
countries specialized in mid- and high-technology sectors than those specialized in natural resources
(especially oil and minerals), which are not so sensitive to the RER. The next two sections will briefly
review the literature concerned with the growth impact of RER misalignments and technology and
reassess its lessons. Section 4 presents and analyzes evidence concerning the relevance of the
development level, before section 5 concludes.
3
Throughout this paper, the term developing countries will include the group of countries referred to as emerging
economies, markets or countries, as well as low-income countries, unless a precise distinction needs to be made. In such a
case, the term emerging economies will refer to developing countries with higher levels of income.
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2. THE IMPACT OF REAL EXCHANGE RATE MISALIGNMENTS ON GROWTH
Nominal devaluations tend to be seen as a tool to correct RERO, which is a RER misalignment
often associated with low levels of growth (Corden and Neary, 1982; Sachs and Warner, 2001).
Therefore, it is relevant to understand how RER misalignments can have an impact on growth. Before
briefly reviewing the strand of literature interested in the effects of RER misalignments on growth in
the second subsection, the main RER equilibrum theories will be briefly presented and analyzed in the
following subsection.
2.1. Main Real Exchange Rate Equilibrium Theories
Since RER misalignment occurs when the RER differs from its equilibrium value, analyzing RER
misalignments is closely related to RER equilibrium theories. The fundamentals approach — one of
the most popular RER equilibrium theories — argues that the RER has reached its equilibrium when
an economy has simultaneously reached its external and internal equilibria (Edwards, 1989, p. 18;
Razin and Collins, 1999, p. 59). On the other hand, according to the purchasing-power-parity (PPP)
theory — in either its strong or weak versions — equilibrium RER remains constant across time,
whereby the law of one price prevails in accordance with its strong version or nominal devaluations
equate with the difference between foreign and domestic inflation as its weak version holds
(Dornbusch, 1985).
Needless to say, the PPP aproach to RER equilibrium has been subject to great criticism; however,
it has evolved by allowing equilibrium RER to no longer be constant. Balassa (1964) and Samuelson
(1964) are often cited as the seminal works providing empirical evidence and developing models that
explain why the relative price levels of non-tradable goods with respect to those of tradable goods —
and thus the equilibrium RER — increase as countries attain a higher development level. Under the
Balassa-Samuelson corrected PPP theory of equilibrium RER, there will be RER misalignments
whenever the domestic price level of a country is either higher or lower than expected given its level
of income per capita.
From the perspective of economic development, the problem with the fundamentals theory of RER
equilibrium is that it considers at least part of real appreciations resulting from commodity booms as a
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movement in a country’s RER towards a new equilibrium, given that the terms of trade are one of the
determinants of a country’s external equilibrium. Therefore, the resulting RER misalignment caused
by improvements in the terms of trade will in principle be lower when measured according to the
fundamentals theory than when following the PPP approach corrected by the Balassa-Samuelson
effect (BSE). A misalignment measure following the latter approach will consider most RER
appreciations as generating RER misalignment, as long as the impact of appreciations on the income
level of the country is not very strong. This means that a RER misalingment measure derived from this
approach will be better able to capture the growth slowdown related to RERO that has been
underscored in the Dutch disease and resource curse literature, as will be further discussed in the next
section.
2.2. Explaining the Impact of Real Exchange Rate Misalignment on Growth: Theories and
Evidence
While there is an extensive body of knowledge concerned with Dutch disease as a theory
explaining the short-term growth impact of RERO and resource curse as a theory explaining the longterm growth impact of RERO, the literature analyzing the growth impact of RERU is rather recent and
builds upon the literature claiming that RERU can be sustained in the medium term (Levy-Yeyati and
Sturzenegger, 2007). Rodrik (2008) expands the arguments of the Dutch disease and resource curse
literature by arguing that RERU should have a positive impact on growth because it implies a higher
relative price of tradables with respect to non-tradables than RER equilibrium warrants. Therefore,
RERU should incentivize investments in the tradable sector and especially within manufacturing —
the author argues — since it increases the return of investors in this sector compared to a situation of
RER equilibrium.
Rodrik (2008, p. 375) estimated a TSCS model of annual growth — averaged over five-year
periods — on initial income and RERU, obtaining empirical evidence supporting the hypothesis that
RERU had a positive impact on growth for developing countries during the 1950–2004 period. In a
similar vein, by running Rodrik’s (2008) growth regressions with different income thresholds to define
developing countries, Rapetti et al. (2012) find evidence in favor of a changing relationship between
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RERU and growth, with RERU having a positive and significant impact for low- and high-income
countries, yet not for middle-income countries.
Rapetti et al. (2012) point out that the non-significance of the impact of RERU on growth in
Rodrik’s (2008) sample of richer economies seems to be driven by its lack of impact in the so-called
emerging economies: a result that puzzles the authors. Nevertheless, Glüzmann, Levy-Yeyati and
Sturzenegger (2012) provide empirical evidence (similarly based on TSCS regressions) supporting the
hypothesis of RERU also having a positive and significant impact on growth in emerging markets.
Nonetheless, it is not clear what criteria the authors employed in classifying a country as an emerging
market.
According to Rodrik’s (2008) theory, it is expected that the impact of RERU on growth will be
lower if the income threshold for the developing country group is increased, since his theoretical
framework assumes that market and government failures affecting the modern tradable sector are less
important for countries at higher income levels. Nevertheless, by using a higher income per capita
threshold to define developed countries, Rapetti et al. (2012) find a significant growth impact in
developed countries and no significant growth impact in middle-income countries. Regarding the
transmission channel between RERU and growth, one can criticize Rodrik’s (2008) lack of match
between theory and evidence, since the industries promoted by RERU in low-income countries
arguably tend not to be part of the mid- or high-technology sectors. Following the results of Rajan and
Subramanian (2011), the manufacturing sectors promoted by RERU in these countries are rather lowtechnology, labor-intensive sectors such as textile, clothing, leather and footwear.
Last but not least, the lack of growth impact of RERU in emerging countries reported in Rapetti et
al. (2012) can be interpreted as evidence in favor of the view that RERU is not a major source of
competitiveness for the industries in which these countries compete, which tend to be more
technologically-intensive. The income differential between emerging and developed countries —
which are the main players in the international market of mid- and high-technology manufactures — is
the base of the cost competitiveness of the former, while their mastery of appropriate DTC should
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allow emerging countries to sustainably grow once their cost competitiveness erodes due to increasing
incomes.
Despite the different growth impacts reported in the studies reviewed in this section, one can argue
that at least a consensus seems to exist concerning the RER equilibrium theory used, i.e. the PPP
approach corrected by the BSE, as well as the econometric approach of TSCS regressions. The
literature review in the next section will attempt to show how authors have underscored the
relationship between development, technology and trade patterns. It is hoped that the insights of this
literature will make clear the need to include DTC together with RERU proxies in the growth
regressions of section 4.
3. DEVELOPMENT, TECHNOLOGY AND TRADE PATTERNS
Theories explaining trade patterns and technological change taking place within developing
countries hold central importance for research like this, which is focused on the topic of growth in the
context of economic development, whereby this last term is understood as the structural change of a
developing country’s economic activities in favor of those more intensive in knowledge (Amsden,
2001, p. 2). The following subsection includes a brief discussion of the role of technology in classical
and neoclassical trade theories, as well as a critique of the early developmentalists to these theories.
The second subsection presents a recent example of a growth theory that considers the importance of
technology for development and presents an indicator for measuring DTC.
3.1. Trade Patterns, Technology, the Prebisch-Singer Hypothesis and Balance-of-Payments
Constraint
In a world with developed and developing countries, the Ricardian model predicts that developed
countries will specialize in high productivity sectors to maintain a high wage level. In the HesckscherOhlin setting, if technology is considered a factor of production, then developed countries should
specialize in the production of technology-intensive goods (Demmou, 2009). Based on these
predictions, one could argue that the optimal international labor division would involve developing
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countries specializing in natural-resource-intensive and low-technology-manufactured goods and
developed countries specializing in mid- and high-technology-manufactured goods.
However, early development economists warned against an apparent secular trend against the
relative prices of primary goods in comparison to manufactures (Prebisch, 1959; Singer, 1950). Such a
negative trend in the terms of trade represents a growth constraint for developing countries exporting
primary goods and importing intermediary and final goods, which can be explained by a low incomeelasticity of demand towards primary goods (Prebisch, 1959, p. 252). Moreover, as pointed out by
Kaldor (1966), the BoP can limit growth in developing countries, given that it can be the source of
imported input and capital bottlenecks or BoP crises, if not properly managed. Furthermore, periods of
commodity price booms (such as during the 2000s) are not necessarily better for the development
perspectives of developing countries specialized in the export of natural resources, since such periods
can increase their natural-resource dependence and hurt their industrial sectors, as analyzed by Cypher
(2010) in the case of South America during the previous decade.
3.2. Domestic Technological Capabilities as Drivers of Economic Growth
DTC can be understood as the result of cost discovery activities (CDA), which Hausmann and
Rodrik (2003, p. 605) define as activities related to the process of ascertaining what a country is good
at producing. CDA relate more to the developing world because finding out the fitness of a country in
terms of producing a given good that is already produced elsewhere relates more to innovations of the
technology assimilation type, while innovations that expand the technological frontier tend to occur in
developed countries (Nelson, 2008, p. 16; Viotti, 2002). However, CDA will be undersupplied in the
absence of government intervention since they generate positive externalities. This means that the
value for a society of discovering the costs of production in new sectors of activity is much greater
than what the first investor in this sector — who performed the CDA — can appropriate as benefits.
This externality can be measured in terms of the benefits captured by copycats once costs have been
‘discovered’ by first movers.
The undersupply of CDA will reduce the growth rate of countries, ceteris paribus. In order to
exploit Gerschenkron’s advantage of backwardness — i.e. being able to achieve high growth rates
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thanks to innovations produced elsewhere — governments in developing countries need to manage the
externality problem generated by CDA. However, intervention needs to go beyond intellectual
property rights regulation, since an important part of CDA will involve the assimilation of
standardized foreign technology, which cannot be patented yet nevertheless requires high learning
investments (Hausmann and Rodrik, 2003, p. 624). An indicator such as the Index of Technological
Specialization (ITS) — defined by Alcorta and Peres (1998) as the ratio of the revealed comparative
advantage of a country in mid- and high-technology-manufacturing sectors over its revealedcomparative advantage in natural-resource-intensive and low-technology-manufacturing industries —
thus seems suited to measure increases in the relative importance of DTC in mid- and high-technology
sectors in the developing world. A developing country that manages to increase the presence of CDA
in its economy will set in motion a process of structural transformation that should increase the value
of its ITS over time, which should not only imply a declining importance of natural-resource-intensive
industries, as Kuznets argued (Niroomand and Nissan, 1991), but also that of low-technology
manufactures.
A key insight of the literature reviewed in this section is the importance of the production structure
— and the trade patterns that it reflects — for the growth prospects of developing countries. This
insight will be empirically analyzed in the next section, together with RERU’s potential for increasing
growth at different development levels. From a theoretical perspective, the main contribution of this
research is the argument that one should not expect constant growth impacts of RERU and DTC across
development levels. From an empirical point of view, it is expected that RERU should have significant
and positive growth impacts at different development levels, albeit of different magnitudes, once DTC
are controlled for.
3.3. The Relationship between Growth and Real Exchange Rate Undervaluation is
Moderated by Domestic Technological Capabilities
One potential reason explaining why the RERU’s positive impact on growth disappears for
developed countries defined with a relatively low GDP per capita threshold in Rodrik (2008) could be
that an important proportion of developing countries with an income level slightly above this threshold
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(the so-called emerging markets) compete in industries that are not so sensitive to RERU, as is the
case with many natural-resource-intensive activities. Moreover, as emerging markets increase their
presence in mid- and high-technology sectors, developed countries will need to depend more on
RERU when competing against emerging countries. As a result, when analyzing the growth impact of
RERU, it seems justifiable to consider three development stages (low, mid and high) and control for
DTC.
The combination of labor abundance — which in theory should facilitate successful competition in
labor-intensive goods — and high RERU levels made low-income developing Asian economies supercompetitive in low-technology-manufactured goods. However, within high-growth-low-incomedeveloping countries, raising income levels can reduce price competitiveness in these sectors. To face
this challenge, governments essentially have two choices: further repress wage growth to achieve high
RERU levels or intervene in favor of building up DTC in mid- and high-technology-manufacturing
sectors (Amsden, 2001, p. 6). Those governments deciding in favor of the latter alternative — while
keeping an eye on RERU and the BoP constraint — seem to have been able to achieve structural
transformation and grow faster. Following the methodological approach used in the literature, TSCS
or panel data regressions will be run in the next section to reassess the impact of RERU and DTC on
growth in developing, emerging and developed countries, once DTC are controlled for.
4. EVIDENCE OF THE RELEVANCE OF THE DEVELOPMENT LEVEL
This section presents and discusses the results of TSCS models used to test the main hypotheses of
this research. One hypothesis is that RERU should have a positive impact on growth across all
development levels (low, mid- and high), once DTC are controlled for. The second hypothesis posits
that the higher the income level of a developing country, the more important the role that DTC in midand high-technology sectors should play as a growth driver.
4.1. Generating the Real Exchange Rate Undervaluation Variable
The need to control for the BSE to obtain an equilibrium level of the RER is illustrated by data
supplied by The Economist (2013) related to the Big Macs index, revealing that Big Macs tend to be
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more expensive in developed countries such as Belgium and Greece than in developing countries such
as Malaysia. Nonetheless, however illustrative of the BSE the price of the Big Mac (a non-tradable
good) might be, a proper RER index needs to be used to generate a RERU measure. Following Rodrik
(2008), I used the nominal exchange rate in local currency units per USD over the PPP exchange rate
in local currency units per international dollars as a measure of the RER.
Nevertheless, data on these variables was taken from a more recent version — 7.0 — of the Penn
World Table (Heston, Summers and Aten, 2011). Last but not least, the approach used here to deal
with data missingness differs from Rodrik’s (2008, p.373), which involved taking five-year averages
of his variables of interest, resulting in only 11 time periods. The several limitations of this approach
include the acute loss of degrees of freedom and the fact that it causes the new averaged dependent
variable to lose variability (Honaker and King 2010, p.562). For these reasons, the multiple imputation
model suggested by Honaker and King (2010) to handle data missingness in both the dependent and
independent variables was followed.
[Table 1]
The results presented in Table 1 are obtained after estimating the following equation, which was
originally proposed by Rodrik (2008, p. 371) and later used within other studies, such as Glüzmann et
al. (2012) and Rapetti et al. (2012):
ln RER 𝑖,𝑡 = 𝛽0 + 𝛽1 ln RGDPCH 𝑖,𝑡 + 𝑦𝑡 + 𝜀𝑖,𝑡
(
(1)
Therefore, the RERU measure obtained is the error term 𝜀𝑖,𝑡 , which constitutes the unexplained
level of RER that cannot be accounted for by a country’s income level (ln RGDPCH 𝑖,𝑡 ) nor by timespecific shocks (𝑦𝑡 ). One salient feature of the results presented in Table 1 is that the BSE estimate
(𝛽̂1 = −0.1291) is almost half the magnitude compared to related studies (Glüzmann et al., 2012;
Rodrik, 2008). The difference in the estimation results could be mainly due to differences in the
sampled time period, the approach to dealing with data missingness, as well as the use of a more
recent data set. Moreover, the results in Table 1 could be criticized due to the model’s low goodness of
fit (0.1046). However, adding more covariates to improve the goodness of fit of the RER model (as
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conducted in the RER fundamentals literature) may lead to arguing that the RER of some countries is
close to equilibrium for given periods, when in fact it might be over- or undervalued, as argued in
section 2.1.
4.2. Results and Discussion
The main hypothesis of this study is that RERU should be a relevant growth driver across
development levels, while the advance of a country’s DTC should play a more important role as a
growth driver in the case of emerging markets with respect to developing countries. The baseline
growth model used to test these sub-hypotheses is as follows:
GROWTHi,t = 𝛽0 + 𝛽1 ln RGDPCH 𝑖,𝑡−1 + 𝛽2 ITS𝑖,𝑡 + 𝛽3 RERU𝑖,𝑡
+ 𝛽4 ITS𝑖,𝑡 × DEV𝑖 + 𝛽5 ITS𝑖,𝑡 × EME𝑖 + 𝛽6 RERU𝑖,𝑡 × DEV𝑖 + 𝛽7 RERU𝑖,𝑡
(2)
× EME𝑖 + 𝑐𝑖 + 𝑦𝑡 + 𝜀𝑖,𝑡
in which GROWTH𝑖,𝑡 is defined as the year-on-year percent change in RGDPCH for country i in year t,
ln RGDPCH𝑖,𝑡−1 is country i’s lagged real GDP per capita level, i.e. the usual converge term in growth
regressions, ITS𝑖,𝑡 is the country’s ITS in year t, RERU𝑖,𝑡 is its real undervaluation measure for the
same year, DEV𝑖 and EME𝑖 are binary variables if country i is a developing country or emerging
market, respectively, and zero otherwise, 𝑐𝑖 is country i’s fixed effect, 𝑦𝑡 is the fixed effect for year t
and 𝜀𝑖,𝑡 is an idiosyncratic error term.
The inclusion of both country and year fixed effects allows us to interpret 𝛽2 and 𝛽3 as the impact
that changes in RERU and the ITS have on the growth rate within each country. The inclusion of
interaction terms allows us to observe whether the impact of these variables differs with respect to the
comparator group of countries, which comprises developed economies. For instance, this means that
the growth impact of RERU in developing countries is given by 𝛽3 + 𝛽6 . The Economic Commission
for Latin America and the Caribbean has a database with ITS values for up to 210 countries and
territories between 1985 and 2004 (Economic Commission for Latin America and the Caribbean
[ECLAC], 2011). Moreover, the remaining variables needed were obtained from the Penn World
Table 7.0 (Heston et al., 2011).
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Last but not least, a recent technique developed to deal with data missingness was taken into
account. A common approach to reducing the bias created by missing data in the empirical literature
investigating RERU’s growth impact is to use five-year averages of the variables of interest in the
TSCS regressions (Rodrik 2008; Glüzmann et al., 2012; Rapetti et al., 2012; Razmi, Rapetti and Skott,
2012). However, averaging values greatly reduces the number of observations available for running
regressions. Since the ITS data is only available for a time span of 20 years, this would have reduced
our T to five, which is too low for TSCS analysis. In order to run TSCS regressions with annual data
given the relative short time span availability of the ITS, the empirical analysis of this research
addressed the issue of data missingness by following the multiple imputation model for TSCS data
proposed by Honaker and King (2010).
After removing growth outliers from the sample, the general message of the results of
specification 6 in Table 2 is in line with the prediction of the hypothesis that RERU as a growth driver
is positive and significant across all development levels, once DTC are controlled for.4 The results of
specification 6 contrast the growth impacts of RERU reported in Rodrik (2008, p.375), who reported a
positive yet not significant coefficient for the case of developed countries, as well as a much larger
positive and significant coefficient for the case of developing countries.
[Table 2]
Moreover, the ITS growth impact proved not to be significant in any country group after removing
the growth outliers and controlling for the lagged dependent (growth) to avoid serial correlation
problems, as can be seen in Table 2. This lack of significance could be due to the existence of some
degree of multicollinearity between GDP per capita and the ITS, which have a correlation coefficient
4
Growth outliers were defined as countries with average growth rates higher than that of Equatorial Guinea, a sparsely
populated country with a low income per capita at the beginning of the time period, which managed to reach a high-income
status by the end of the period thanks to the discovery of oil fields (Frynas, 2004). No similar narratives could be found for
countries with growth rates above that of Equatorial Guinea. The existence of growth outliers might be due to the multiple
imputation algorithm performing poorly for some countries’ growth rates due to the high level of missingness of the GDP per
capita of these outliers. Moreover, following Rodrik (2008, p.377), all countries with an average GDP per capita lower than
USD 6,000 in PPP 2005 terms for the period analyzed are classified as developing countries. Emerging economies are
countries with an average GDP per capita equal or above USD 6,000 yet below USD 10,725. Countries above the upper
bound of this income range — which was the threshold used by the World Bank in 2005 for the high-income group (World
Bank, 2012) — are classified as developed countries.
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EXCHANGE RATE AND TECHNOLOGY
of 0.36. Generating an ITS corrected for a country’s development level could address this problem.
Moreover, this transformation addresses endogeneity issues between GDP per capita growth and the
ITS, since growth is a factor driving technical change according to Kaldor-Verdoorn’s law (Porcile
and Lima, 2010).
The results of specification 8 in
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ALEJANDRO MÁRQUEZ-VELAZQUEZ
Table 3 provide statistical evidence supporting the hypothesis that increases in the developmentlevel corrected ITS (DCITS) have a negative impact on growth in developing countries, ceteris
paribus.5 This provides evidence in favor of the idea that increased competitiveness in mid- and hightech sectors beyond what can be expected for developing countries is probably related to excessive
processed exports in these sectors. Furthermore, the result points to the idea that developing countries
following a growth-model based on cheap labor in the labor-intensive sections of mid- and hightechnology manufacturing industries are actually hurting their growth prospects. This result is
consistent with Jarreau and Poncet (2012), who find that processed exports have a small growth
contribution in China.
[Table 3]
The short-run growth impact of increases in the DCITS in developed countries (0.011) is positive
and not significant, as well as about half the size of the equally non-significant coefficient of the ITS
for developed countries. This means that correcting the ITS for the development level of countries did
not result in increased DTC in mid- and high-technology sectors having significant growth impacts in
developed countries. Moreover, the interaction between the developing country dummy and the DTC
proxies is negative in specifications 6 and 8, albeit it is only significant when DCITS is used. Last but
not least, the interaction between the DTC proxies and the emerging market dummy is positive and not
significant in both specifications, yet its magnitude doubles in specification 8.
All these results provide evidence in favor of the view that increases in DTC in mid- and hightechnology sectors were slightly more relevant for growth in emerging markets than in developed
countries between the mid-1980s and mid-2000s. Moreover, the results suggest that developing
countries that specialized in natural resource-intensive and low-technology manufacturing sectors had
slightly higher growth rates, ceteris paribus. Regarding the growth impact of RERU, it was positive
and significant in specifications 6 and 8 for developed countries, while its growth impact in
5
The DCITS was generated following a similar method as used for the RERU variable.
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EXCHANGE RATE AND TECHNOLOGY
developing and emerging countries is somewhat lower, albeit still significant. Nevertheless, it is only
significantly lower in the case of emerging markets.
5. CONCLUSION
The results obtained in section 4 provide clearer support in favor of the hypothesis related to the
growth impacts of RERU across development levels than for the hypothesis related to the different
growth impacts of DTC. This suggests that the empirical evidence in favor of the hypothesis that
RERU should also be positive and significant for the case of emerging markets was robust. By
contrast, the evidence supporting the hypothesis that the growth impact of increases in DTC in midand high-technological manufacturing sectors should be greater in emerging economies with respect to
developing countries was less conclusive. Nonetheless, the results support the idea that RERU should
also play an important role in the development strategies of emerging markets. Finally, as the results
suggest that processed exports of mid- and high-technology manufactures negatively affect growth in
developing countries, analyzing the direction of trade of these types of manufactures in developing and
emerging countries seems a promising future research endeavor that could help to shed light on
whether the direction of trade is a relevant characteristic to make the DTC proxies’ growth impact
significant.
18
ALEJANDRO MÁRQUEZ-VELAZQUEZ
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Table 1. The effect of gross domestic product per capita increases on the real exchange rate, 1985-2004
Independent variable
Intercept
1.5348***
(0.0824)
GDP per capita (in logs, PPP 2005 USD)
-0.1291***
(0.0101)
% of significant year fixed effects
53%
Observations
3,820
(N=191, T=20)
Adjusted R2
LM test p-value
0.1046
1.2633 x 10-178
Notes: Panel corrected standard errors in parenthesis; *** p-value<0.01; ** 0.01<p-value<0.05; * 0.05<p-value<0.10.
Year fixed effects reported as significant when their p-value<0.10.
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EXCHANGE RATE AND TECHNOLOGY
Table 2. The impact of real exchange rate undervaluation and the index of technological specialization on growth,
1986–2004a
Independent variables
Specification number
(1)
(2)
(3)
(4)
(5)
(6)
24.2***
25.16***
25.11***
25.58***
1.083**
0.92*
(3.42)
(3.35)
(3.38)
(3.45)
(0.483)
(0.499)
-3.91***
-4.07***
-4.07***
-4.12***
-0.173**
-0.151*
(0.55)
(0.55)
(0.55)
(0.58)
(0.078)
(0.081)
2.24**
2.25**
2.82*
0.026*
0.021
(1.12)
(1.14)
(1.12)
(0.015)
(0.016)
0.2
0.28
-0.1
0.057**
0.067**
(0.69)
(0.71)
(1.56)
(0.027)
(0.027)
-2.73
-0.028*
-0.024
(1.52)
(0.016)
(0.017)
0.91
0.012
0.007
(2.89)
(0.011)
(0.012)
0.35
-0.029
-0.027
(1.26)
(0.036)
(0.034)
0.68
-0.04
-0.052*
Intercept
Lagged GDP per capita
(in logs, PPP 2005 USD)
ITS
RERU
ITS x developing country
dummy
ITS x emerging economy
dummy
RERU
x
developing
country dummy
RERU
x
emerging
economy dummy
(1.43)
(0.029)
(0.028)
-0.007
Lagged Growth
(0.123)
% of significant country
94
93
93
89
85
78
6
6
6
6
6
59
3,629
3,629
3,629
3,629
3,002
2,844
fixed effects
% of significant year
fixed effects
Observations
Adjusted R-Square
(N=191,
(N=191,
(N=191,
(N=191,
(N=158,
(N=158,
T=19)
T=19)
T=19)
T=19)
T=19)
T=18)
0.18
0.2
0.2
0.2
0.12
0.13
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ALEJANDRO MÁRQUEZ-VELAZQUEZ
LM test p-value
0.17
0.35
0.35
0.27
0
0.22
Notes: Panel corrected standard errors in parenthesis; *** p-value<0.01; ** 0.01<p-value<0.05; * 0.05<p-value<0.10.
a
Country and year fixed effects reported as significant when their p-value<0.10. Specifications 6 and 7 were regressed with
158 countries and the time frame of specification 7 was 1987–2004.
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EXCHANGE RATE AND TECHNOLOGY
Table 3. The impact of real exchange rate undervaluation and the development-level corrected index of technological
specialization on growth in 158 countries between the mid-1980s and mid-2000s
Independent variables
Intercept
Lagged GDP per capita (in logs, PPP 2005 USD)
Development-level corrected ITS (DCITS)
RERU
DCITS x developing country dummy
DCITS x emerging economy dummy
RERU x developing country dummy
RERU x emerging economy dummy
Specification number
(7)
(8)
1.103**
0.941*
(0.485)
(0.503)
-0.176**
-0.155*
(0.078)
(0.081)
0.015
0.011
(0.01)
(0.011)
0.058**
0.068**
(0.027)
(0.027)
-0.051*
-0.045*
(0.02)
(0.022)
0.020
0.015
(0.013)
(0.014)
-0.033
-0.03
(0.036)
(0.034)
-0.040
-0.054*
(0.042)
(0.028)
Lagged Growth
-0.009
(0.122)
% of significant country fixed effects
85
78
26
ALEJANDRO MÁRQUEZ-VELAZQUEZ
% of significant year fixed effects
Observations
6
59
3,002
2,844
(N=158, T=19)
(N=158, T=18)
Adjusted R-Square
0.12
0.13
LM test p-value
0.002
0.329
1986-2004
1987-2004
Time frame
Notes: Panel corrected standard errors in parenthesis; *** p-value<0.01; ** 0.01<p-value<0.05; * 0.05<p-value<0.10.
Country and year fixed effects reported as significant when their p-value<0.10.